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Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products

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  • Zhou, Jianxiong
  • Wei, Shanbi
  • Chai, Yi

Abstract

The flexible test system (FTS) for aerospace pyromechanical device products (PDPs) is an important mean to accurately assess the quality of aerospace-grade PDPs. Since PDPs are disposable products produced in small batch and multi-batch, only a limited number of samples can be tested for each batch to evaluate the quality level of whole batch of products. Therefore, the reliability of ignition FTS is concerned to ensure the reliability of limited test samples. To meet the test requirements of PDPs, the ignition FTS adopts a flexible method to adapt to the different batch types and models. Besides, the reliability of ignition FTS is related to time and product batch task, the reliability evaluation method based on traditional Bayesian network can't describe the dynamic process of product batch. Therefore, this paper proposes a dynamic Bayesian network method based on the combination of time and batch factors to overcome the limitations of product batch change, and a channel configuration optimization method is introduced to meet the reliability requirements, which provides an effective mean for evaluating the FTS reliability. Numerical experiments show that the method can accurately reflect the reliability change of FTS for aerospace PDPs.

Suggested Citation

  • Zhou, Jianxiong & Wei, Shanbi & Chai, Yi, 2021. "Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000703
    DOI: 10.1016/j.ress.2021.107508
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    References listed on IDEAS

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    1. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Lu, Qin & Zhang, Wei, 2022. "Integrating dynamic Bayesian network and physics-based modeling for risk analysis of a time-dependent power distribution system during hurricanes," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Caetano, Henrique O. & N., Luiz Desuó & Fogliatto, Matheus S.S. & Maciel, Carlos D., 2024. "Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Xiao, Yong & Wei, Shanbi & Chai, Yi & Pan, Tianle & Hou, Yang, 2023. "Reliability optimization of flexible test system based on pyro-mechanical device products production driven," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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